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    <title>Demo Title Attention and Augmented Recurrent Neural Networks</title>
  
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  </head><body><distill-header></distill-header><d-front-matter>
    <script type="text/yml">
      title: Demo Title Attention and Augmented Recurrent Neural Networks
      published: Jan 10, 2017
      authors:
      - Chris Olah:
      - Shan Carter: http://shancarter.com
      affiliations:
      - Google Brain:
      - Google Brain: http://g.co/brain
    </script>
  </d-front-matter>



<d-article>
  <d-title>
    <h1>Attention and Augmented Recurrent Neural Networks</h1>
    <!-- <h2>Some people want a deck</h2> -->
    <d-byline></d-byline>
  </d-title>

  <d-abstract>
    <p>This is the ﬁrst paragraph of the article. Test a long — dash — here it is.</p>
  </d-abstract>
  <p>This is the ﬁrst paragraph of the article. Test a long — dash — here it is.</p>
  <p>Test for owner’s possessive. Test for “quoting a passage.” And another sentence. Or two. Some ﬂopping ﬁns; for diving.</p>
  <p>Here’s a test of an inline equation <d-math>c = a^2 + b^2</d-math>. And then there’s a block equation:</p>
  <d-math block="">
      c = \pm \sqrt{ \sum_{i=0}^{n}{a^{222} + b^2}}
  </d-math>
  <p>We can<d-cite key="mercier2011humans"></d-cite> also cite <d-cite key="gregor2015draw,mercier2011humans"></d-cite> external publications. <d-cite key="dong2014image,dumoulin2016guide,mordvintsev2015inceptionism"></d-cite></p>
  <p>We should also be testing footnotes<d-footnote>This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote.</d-footnote>. There are multiple footnotes, and they appear in the appendix<d-footnote>Given I have coded them right. Also, here’s math in a footnote: <d-math>c = \sum_0^i{x}</d-math>. Also, a citation. Box-ception<d-cite key="gregor2015draw"></d-cite>!</d-footnote> as well.</p>
  <table>
    <thead>
      <tr><th>First</th><th>Second</th><th>Third</th></tr>
    </thead>
    <tbody>
      <tr><td>23</td><td>654</td><td>23</td></tr>
      <tr><td>14</td><td>54</td><td>34</td></tr>
      <tr><td>234</td><td>54</td><td>23</td></tr>
    </tbody>
  </table>
  <h2>Displaying code snippets</h2>
  <p>Some inline javascript:<d-code language="javascript">var x = 25;</d-code></p>
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  <d-code block="" language="javascript">
      var x = 25;
      function(x){
        return x * x;
      }
  </d-code>
  <p>We also support python.</p>
  <d-code block="" language="python">
    # Python 3: Fibonacci series up to n
    def fib(n):
      a, b = 0, 1
        while a &lt; n:
          print(a, end=' ')
          a, b = b, a+b
  </d-code>
  <p>That’s it for the example article!</p>
</d-article>

<d-appendix>
  <d-acknowledgements>
    <h3>Contributions</h3>
    <p>Some text describing who did what.</p>
    <h4>Reviewers</h4>
    <p>Some text with links describing who reviewed the article.</p>
  </d-acknowledgements>

  <d-footnote-list></d-footnote-list>

  <d-bibliography>
    <script type="text/bibtex">

      @article{gregor2015draw,
        title={DRAW: A recurrent neural network for image generation},
        author={Gregor, Karol and Danihelka, Ivo and Graves, Alex and Rezende, Danilo Jimenez and Wierstra, Daan},
        journal={arXiv preprint arXiv:1502.04623},
        year={2015},
        url ={https://arxiv.org/pdf/1502.04623.pdf}
      }
      @article{mercier2011humans,
        title={Why do humans reason? Arguments for an argumentative theory},
        author={Mercier, Hugo and Sperber, Dan},
        journal={Behavioral and brain sciences},
        volume={34},
        number={02},
        pages={57--74},
        year={2011},
        publisher={Cambridge Univ Press},
        doi={10.1017/S0140525X10000968}
      }

      @article{dong2014image,
        title={Image super-resolution using deep convolutional networks},
        author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
        journal={arXiv preprint arXiv:1501.00092},
        year={2014},
        url={https://arxiv.org/pdf/1501.00092.pdf}
      }

      @article{dumoulin2016adversarially,
        title={Adversarially Learned Inference},
        author={Dumoulin, Vincent and Belghazi, Ishmael and Poole, Ben and Lamb, Alex and Arjovsky, Martin and Mastropietro, Olivier and Courville, Aaron},
        journal={arXiv preprint arXiv:1606.00704},
        year={2016},
        url={https://arxiv.org/pdf/1606.00704.pdf}
      }

      @article{dumoulin2016guide,
        title={A guide to convolution arithmetic for deep learning},
        author={Dumoulin, Vincent and Visin, Francesco},
        journal={arXiv preprint arXiv:1603.07285},
        year={2016},
        url={https://arxiv.org/pdf/1603.07285.pdf}
      }

      @article{gauthier2014conditional,
        title={Conditional generative adversarial nets for convolutional face generation},
        author={Gauthier, Jon},
        journal={Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester},
        volume={2014},
        year={2014},
        url={http://www.foldl.me/uploads/papers/tr-cgans.pdf}
      }

      @article{johnson2016perceptual,
        title={Perceptual losses for real-time style transfer and super-resolution},
        author={Johnson, Justin and Alahi, Alexandre and Fei-Fei, Li},
        journal={arXiv preprint arXiv:1603.08155},
        year={2016},
        url={https://arxiv.org/pdf/1603.08155.pdf}
      }

      @article{mordvintsev2015inceptionism,
        title={Inceptionism: Going deeper into neural networks},
        author={Mordvintsev, Alexander and Olah, Christopher and Tyka, Mike},
        journal={Google Research Blog},
        year={2015},
        url={https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html}
      }

      @misc{mordvintsev2016deepdreaming,
        title={DeepDreaming with TensorFlow},
        author={Mordvintsev, Alexander},
        year={2016},
        url={https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb},
      }

      @article{radford2015unsupervised,
        title={Unsupervised representation learning with deep convolutional generative adversarial networks},
        author={Radford, Alec and Metz, Luke and Chintala, Soumith},
        journal={arXiv preprint arXiv:1511.06434},
        year={2015},
        url={https://arxiv.org/pdf/1511.06434.pdf}
      }

      @inproceedings{salimans2016improved,
        title={Improved techniques for training gans},
        author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
        booktitle={Advances in Neural Information Processing Systems},
        pages={2226--2234},
        year={2016},
        url={https://arxiv.org/pdf/1606.03498.pdf}
      }

      @article{shi2016deconvolution,
        title={Is the deconvolution layer the same as a convolutional layer?},
        author={Shi, Wenzhe and Caballero, Jose and Theis, Lucas and Huszar, Ferenc and Aitken, Andrew and Ledig, Christian and Wang, Zehan},
        journal={arXiv preprint arXiv:1609.07009},
        year={2016},
        url={https://arxiv.org/pdf/1609.07009.pdf}
      }

    </script>
  </d-bibliography>

  <distill-appendix> </distill-appendix>
</d-appendix>

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